A hand gesture recognition system for American sign language (ASL) using hierarchical features based on an infrared image is proposed. To reduce the error rate and illumination chage, the infrared image is used in this article. Hierarchical features consist of object extern, Hu-moment invariants, and direction features. First, circularity and eccentricity can be computed from the object extern feature. And then, ASL is classified by K-means using them. Next, the moment invariants features are used to recognize hand gestures by back-propagation (BP). Finally, the direction feature can accurately classify similar gestures like G and Z, I and J, U and H. The goal of this article is to achieve an efficient and effective hand gesture recognition system that meets the high recognition rate of gestures. Through experiments, the recognition rate for the proposed method is 97.15% and it takes 0.046 s to process one frame.
목차
Abstract 1. Introduction 2. Relevant Theories 2.1. Hu-Moment Invariants 2.2. Back-Propagation 3. The Proposed Method 3.1. Preprocessing for Hand Region 3.2. Hierarchical Features 4. Experimental Results 4.1. Experimental Environment 4.2. Experimental Results 5. Conclusions Acknowledgments References
보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Multimedia and Ubiquitous Engineering
간기
월간
pISSN
1975-0080
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Multimedia and Ubiquitous Engineering Vol.10 No.9